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According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
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Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
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The sampling variability of a statistic is defined as how much the statistic varies from one sample to another. The sampling variability of a statistic is typically measured by measuring its standard error.
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Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
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Using a Split-belt Treadmill to Evaluate Generalization of Human Locomotor Adaptation
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Human-agent co-adaptation using error-related potentials.

Stefan K Ehrlich1, Gordon Cheng1

  • 1Chair for Cognitive Systems, Department of Electrical and Computer Engineering, Technical University of Munich, Arcisstrasse 21, 80333 Munich, Germany.

Journal of Neural Engineering
|September 12, 2018
PubMed
Summary
This summary is machine-generated.

Error-related potentials (ErrPs) from electroencephalogram (EEG) feedback enable robots to adapt to human partners in collaborative scenarios. This research demonstrates ErrPs effectively mediate mutual adaptation, improving human-robot interaction (HRI).

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Area of Science:

  • Robotics
  • Neuroscience
  • Human-Computer Interaction

Background:

  • Error-related potentials (ErrPs) are derived from electroencephalogram (EEG) and offer intuitive feedback for human-robot interaction (HRI).
  • Previous work focused on ErrPs for robot skill learning, not mutual adaptation in collaborative HRI.
  • Collaborative HRI requires robots to be perceived as intentional agents capable of mutual adaptation.

Purpose of the Study:

  • To explore the usability of ErrPs as a feedback signal for mediating co-adaptation in HRI.
  • To investigate ErrPs in scenarios requiring mutual adaptation between humans and robots.
  • To extend ErrP research to less constrained HRI settings.

Main Methods:

  • An experimental study demonstrated ErrPs mediating co-adaptation in a consensus-driven human-robot interaction.
  • Human subjects adapted to robot behavior, while the robot adapted based on online ErrP decoding from human EEG.
  • ErrPs were decoded in real-time with high accuracy (81.8% ± 8.0%) across 13 subjects.

Main Results:

  • Online ErrP decoding was accurate enough for effective robot adaptation.
  • Successful co-adaptation led to significant improvements in HRI efficacy and efficiency.
  • The study demonstrated emergent robot behaviors resulting from co-adaptation.

Conclusions:

  • ErrPs show potential as a valuable feedback signal for mediating co-adaptation in HRI.
  • This research extends ErrP applications to scenarios with mutual human-robot adaptation.
  • ErrPs can help align robot behavior with human preferences in increasingly embedded robotic systems.